Autonomous Vehicle Emergency Coordination via Object Movement Prediction
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Solution Overview
Problem
Self-driving vehicles face challenges in efficiently handling emergency situations while minimizing risk to the public, as they need to perform functions beyond standard driving, such as evacuating or distracting hazards, which requires advanced object detection and movement prediction capabilities.
Innovation Solution
A system and method in self-driving vehicles that process image data to detect objects, determine a target object based on movement patterns, and perform operations like pursuing or communicating with other vehicles and traffic signals to manage emergency situations effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If self-driving vehicles perform aggressive operations in emergency situations, then emergency handling efficiency is improved, but safety risk increases
Solution Approach 1:
The system performs preliminary actions by predicting the movement of target objects before executing emergency operations. The prediction module forecasts future positions and trajectories of detected objects, allowing the vehicle to plan and prepare aggressive maneuvers in advance while ensuring they are executed safely. This preliminary prediction enables the vehicle to assess potential risks before committing to high-speed emergency operations.
2Adaptability or versatility
If self-driving vehicles perform functions beyond standard driving in emergency situations, then emergency response capability is improved, but system complexity increases
Solution Approach 1:
The emergency response system is segmented into distinct functional modules: an object detection module that identifies targets, a movement prediction module that forecasts trajectories, and an emergency operation module that executes maneuvers. This segmentation allows each module to specialize in specific tasks, improving overall emergency response capability while managing system complexity through modular design. Each module can be independently optimized and tested.
3Measurement precision
If self-driving vehicles use advanced object detection and movement prediction capabilities, then emergency handling accuracy is improved, but computational requirements increase
Solution Approach 1:
The system applies partial action by focusing computational resources on predicting only the critical movement parameters of detected objects (position, velocity, trajectory) rather than analyzing all possible physical properties. This selective prediction approach achieves sufficient accuracy for emergency operations while reducing computational load and energy consumption compared to comprehensive object analysis.
Data Source
AI summary
A system included and a computer-implemented method performed in one of a plurality of self-driving vehicles that are connected through a network are described. The system performs: processing image data of one or more scene images received by said one of the plurality of self-driving vehicles, to detect one or more objects included in the one or more scene images; determining a target object from the one or more detected objects at least based on the processed image data; predicting movement of the target object at least based on a current position and a current movement state of the target object; and performing a self-driving operation to drive said one of the plurality of self-driving vehicles based on the predicted movement of the target object.